The present embodiments relate to image correction. Images often include artifacts. The image may be restored by reducing the artifacts. Image restoration tasks, such as denoising, deblurring, inpainting, or reconstruction are inverse problems in which a degradation operator was applied to the image. The original image is recovered by combining a physical model of the degradation behavior and a prior model of what a good image should be. Image restoration is typically cast as a Maximum A Posteriori (MAP) estimation problem, then solved using constrained optimization.
Prior approaches to solve for this inverse problem include manually crafting a prior model and an algorithm to provide a good compromise between the reconstruction quality and the complexity of the algorithm. These prior approaches suffer from simplistic assumptions to reduce the complexity of the manual prior and from the requirement for balancing this compromise that may be different for many different situations. Another approach uses supervised machine learning to output a corrected image given an input image. The machine-learning approach suffers from a lack of availability of a sufficiently large set of distortion and imaging system-specific training data and from poor generalization: if the distortion, then the mapping must be re-trained from scratch although the observed object is the same.